optiwindnet.MILP.ortools
========================

.. py:module:: optiwindnet.MILP.ortools




Module Contents
---------------

.. py:function:: make_min_length_model(A: networkx.Graph, capacity: int, *, topology: optiwindnet.MILP._core.Topology = Topology.BRANCHED, feeder_route: optiwindnet.MILP._core.FeederRoute = FeederRoute.SEGMENTED, feeder_limit: optiwindnet.MILP._core.FeederLimit = FeederLimit.UNLIMITED, balanced: bool = False, max_feeders: int = 0) -> tuple[ortools.math_opt.python.mathopt.Model, optiwindnet.MILP._core.ModelMetadata]

   Make discrete optimization model over link set A.

   Build OR-tools CP-SAT model for the collector system length minimization.

   :param A: graph with the available edges to choose from
   :param capacity: maximum link flow capacity
   :param topology: one of Topology.{BRANCHED, RADIAL}
   :param feeder_route: FeederRoute.SEGMENTED -> feeder routes may be detoured around subtrees;
                        FeederRoute.STRAIGHT -> feeder routes must be straight, direct lines
   :param feeder_limit: one of FeederLimit.{MINIMUM, UNLIMITED, SPECIFIED,
                        MIN_PLUS1, MIN_PLUS2, MIN_PLUS3}
   :param max_feeders: only used if feeder_limit is FeederLimit.SPECIFIED


.. py:function:: warmup_model(model: ortools.math_opt.python.mathopt.Model, metadata: optiwindnet.MILP._core.ModelMetadata, S: networkx.Graph) -> ortools.math_opt.python.mathopt.Model

   Set initial solution into `model`.

   Changes `model` and `metadata` in-place.

   :param model: CP-SAT model to apply the solution to.
   :param metadata: indices to the model's variables.
   :param S: solution topology

   :returns: The same model instance that was provided, now with a solution.

   :raises OWNWarmupFailed: if some link in S is not available in model.